3 The in-memory computing era The conventional approach to computer processing was based on storing data on the hard disk and retrieving them when performing a task on the computer s main memory, which is where computations happen. Nowadays, this traditional approach has been supplemented by the emergence of so-called inmemory computing (IMC), a new processing model in which the primary location for application data is the computer s main memory, not external storage devices. In other words, in-memory technology eliminates the need to run a disk-seek operation each time a data look-up is performed, making data access latency negligible. This leads to greater system performance and holds the potential for dramatic application innovation: opening the way to a new computing era, characterized by faster and smarter access to data. Two main factors are contributing to the dramatic evolution of in-memory computing: The 64-bit processors By moving from a 32-bit to a 64-bit addressing scheme, the addressable memory space grows exponentially from 2 32 to 2 64 and the theoretical address space peak becomes 16 exabytes extremely large numbers for a practically unlimited amount of memory. In reality, not all the available space can be utilized for storing data, but the upper limit of the Windows system cache size grows from 1GB in 32 bits to 1TB in 64 bits, a thousand-time increase. ARCHITECTURAL COMPONENT 64-BIT WINDOWS 32-BIT WINDOWS Virtual memory 16 Terabytes 4 Gigabytes System cache 1 Terabyte 1 Gigabyte Data source: Microsoft support site The continuous RAM price decrease In the last 30 years RAM prices have decreased by over 500,000 times: in 1981 RAM was costing $ $4.00 per KB, today 8GB of RAM per imac costs between $30.00 and $40.00 ( c per KB). And the trend does not appear to be stopping anytime soon: analysts expect that DRAM memory prices will decrease by 5 times from 2011 to RAM Price per Megabyte (Logarithmic Scale) THE NEW FRONTIER OF IN-MEMORY BUSINESS INTELLIGENCE AND CPM 3

4 In-Memory CPM and BI: shortcomings If the advantages of in-memory computing are evident in terms of performance, it is important to note how the full adoption of this technology so far has resulted in some functional limits of particular relevance in the area of Business Intelligence (BI) and Corporate Performance Management (CPM). Unsupported write-back The vast majority of in-memory products on the market are not designed to support writeback and consequently do not provide a real ability to perform what-if analysis and manage multidimensional calculations. In this approach, the data remains a static and unchanging entity, thereby precluding its use for any simulation and planning process. Inflexible data model The main stand-alone in-memory technologies do not allow you to change the data structure on the fly neither by inserting new occurrences (i.e. a product in a budget process) nor by modifying the data model (i.e. introducing a new categorization of products or creating a reclassification in the chart of accounts). Any changes require a database reload to be carried out making it impossible to manage any structured simulation or planning process. Narrow functional scope The main stand-alone in-memory solutions that are currently on the market are good tools for interactive visualization and fast analysis of static contents, but are absolutely lacking whenever you need to actively manage data, or create applications for BI and CPM. The absence of an integrated workflow, the inability to document data with notes or attachments, the lack of a system of business rules, and the often rudimentary integration with Microsoft Office, are all characteristics that make them unusable for handling articulated Business Intelligence implementations or planning and simulation processes. Inefficient high user concurrency The stand-alone in-memory solutions typically prove themselves inefficient in managing large numbers of concurrent users: in fact, the absence of any model of pre-aggregation of data results in a strong demand for additional RAM resources as the number of users grows. Although it is impossible to make a general assessment valid for all products, it is estimated that any additional concurrent license requires around a 10% increase in the RAM resources to maintain the same performance. 4 THE NEW FRONTIER OF IN-MEMORY BUSINESS INTELLIGENCE AND CPM

5 Limited data scalability The scalability of the stand-alone in-memory solution is clearly limited by the RAM that is available. Theoretically, in-memory solutions have a terabyte amount of addressable memory available but as the number of concurrent users increases, this amount of memory is considerably reduced, therefore strongly influencing the available data space (see inefficient high user concurrency). The problems worsen once the hardware limit (physical or virtual) is reached: when all data cannot be loaded into the memory, workarounds or heavy implementation compromises (e.g. partitioning of data across multiple data mart models) become necessary severely limiting the efficiency of stand-alone in-memory solutions with the increase of users and data volume. Sealed applications The main stand-alone in-memory solutions are characterized by the inability to access data outside the data mart that is mapped and utilized in memory. In practice, when imported in RAM, data is sealed in a box making it impossible to create any connection with external sources. This approach inhibits all types of drill-through and of direct connections to external databases and data-warehouses, making it impossible to not only gain real-time access to data but to also create smart applications where the aggregated analyses are managed in-memory and the detailed information (i.e. invoice line) is left in the original database and accessed only when needed. Furthermore, the lack of any distinction between data and applications (they are stored in the same physical files) makes it very difficult to connect different applications and impossible to share a single data repository across the enterprise reinforcing, once again, the problem with silos of information. Poor administration of complex environments The process of data management solutions in full-memory is perceived as very simple: as if the data was loaded into memory it all worked automatically. In reality, an important staging phase becomes necessary with the increasing complexity and scope of the databases used, which involves a huge amount of preparation to create a data-mart that is usable by in-memory solutions. Moreover, the absence of data pre-modeling, which is effective for the management of simple non-interconnected environments, becomes a serious limitation for the development of crossfunctional or inter-departmental applications, in which unique data must be shared for different types of analysis. With the need to manage increasing complexity, the lack of data pre-modeling quickly turns from an advantage to a disadvantage, making it very difficult to govern enterprise environments. Cost Although the cost of RAM has fallen in recent years, in-memory computing continues to have significantly higher costs than traditional disk-based solutions. The adoption of a stand-alone in-memory solution often means managing a series of activities, on which the marginal benefits of in-memory computing are irrelevant, such as static reporting with very expensive technology with the final result unnecessarily increasing the overall total cost of ownership of BI and CPM projects. THE NEW FRONTIER OF IN-MEMORY BUSINESS INTELLIGENCE AND CPM 5

6 HBMP: the new frontier of in-memory BI and CPM BOARD s approach to in-memory technology started with a precise goal: to exploit the full potential of IMC (in-memory computing) in terms of better performance without sacrificing the flexibility, scalability, total cost of ownership and ability to effectively support the simulation and planning processes, that have fueled international success for BOARD. From this research, a unique technology arose which BOARD has called HBMP that has no equal either in the Business Intelligence or in the Corporate Performance Management space. What is HBMP (Hybrid Bitwise Memory Pattern)? Bitwise Memory Pattern Thanks to the adoption of a new algorithm, physical data is stored and mapped through unique bits, or rather bitmapped. In other words, BOARD maps the compressed multidimensional data structure (Pattern) at bit level (Bitwise) into the RAM (Memory). The new algorithm, designed to exploit all the potential of in-memory computing, brings enormous benefits, not only in terms of performance, but also in terms of data compression, parallelism and high-user concurrency. Furthermore, unlike what happens with the majority of in-memory solutions, the algorithm is designed to ensure full bi-directionality (read-write) in the use of information. Consequently, this allows full support of write-back and on the fly changes of the data structure, thus keeping all the functional capabilities of BOARD intact. Hybrid. Another fundamental characteristic of HBMP technology is its Hybrid nature, or rather the ability to manage data in three different ways: Full in memory To do all processing against data held in-memory On-demand in-memory To decide which subset of data to use in-memory as more appropriate to the workload (In-memory metadata for read-only operations; dynamic upload of physical data for writeback) Hybrid in-memory To store in-memory data indices and mapping and to leave data on the disk These three different ways of configuring the BOARD in-memory environment allows great flexibility in choosing the model that best suits your needs, both in terms of scalability and priceperformance. Not only is any out of memory problem eliminated, but at the same time, BOARD offers all the options needed to optimize the technological cost of ownership of any BI and CPM implementation. 6 THE NEW FRONTIER OF IN-MEMORY BUSINESS INTELLIGENCE AND CPM

7 Why HBMP makes a difference HBMP overcomes the limitations of traditional stand-alone BI technologies, combining pure performance with functional and technological features, which differentiates it from any other solution currently available on the market. Functional Benefits Full support to advanced write-back One of the most important characteristics of the HBMP technology is the full support provided to write-back, both in terms of read-write and in the distribution of the data changes through different levels of aggregation (i.e. a change in the expected turnover of a city automatically modifies that of the province, region and nation, as well as all the customers and products that contribute to determining its value), and in terms of handling of calculated fields (i.e. a change within revenue is automatically reflected in prices and quantities). This ability differentiates BOARD from other in-memory read only solutions, especially from those that provide a minimum capacity of what-if analysis (the change of a single parameter and recalculation), but do not guarantee a full capacity of distributing the changes or the ability to modify calculated fields, making it impossible to effectively manage simulation and budget processes. BOARD s superiority becomes even more evident when considering the ability to model business processes. A series of dedicated procedures makes it easy to handle allocations, consolidations, phasing and more general sets of primary business activities, which are not managed even at a basic level in the main stand-alone in-memory solutions. Flexible data model Another key feature of the HBMP technology is the power to modify the data model after it has been mapped in-memory. From a practical point of view this means the ability to embed new elements of the budget (i.e. new product, customer, item of expenditure, etc.) and new categorizations (i.e. change in the sales areas or product groups) on an on the fly basis during simulation and planning processes without having to reload all the data in RAM, as is typical with the stand-alone in-memory solutions. Extended functional coverage HBMP technology is designed to combine the advantages of in-memory computing with all the application functionality previously offered by BOARD. The end result is not only a solution for fast data analysis and visualization, but a comprehensive software platform that combines extreme speed of analysis with integrated work-flow, business modeling, data documentation and advanced office integration capabilities, always ensuring full support to write-back. Unlimited scalability The three in-memory configuration options offered by HBMP technology (full in-memory, ondemand in-memory, hybrid in-memory) and the ability to move easily from one to another, make BOARD s scalability virtually unlimited. In particular, the hybrid in-memory mode allows for maximum scalability without sacrificing high performance. Thanks to a strong optimization in the use of RAM, which is not overloaded with the physical data but is only used to manage metadata (dimensional structures and their mapping), it is possible to achieve a far superior scalability than the one obtainable when loading all the data in RAM, with a relatively small, loss of performance (around 10%-15% in an environment with ordinary high user concurrency). THE NEW FRONTIER OF IN-MEMORY BUSINESS INTELLIGENCE AND CPM 7

8 Extensibility Thanks to its in-memory metadata processing capability, HBMP technology enables the data-mart that is managed in-memory to be connected in a transparent way with data residing on disk or external DBs. From a technical standpoint, this ability ensures full support to any drill-through activitie and permits the implementation of in-memory applications with direct access to external data. This makes it possible to not only access real-time data from an in-memory application, but to also create smart applications where the aggregated analyses are managed in-memory, while the detailed information (i.e. invoice line), are left in the original database and accessed only when needed. Ease of administration The HBMP technology encapsulates the main advantages of MOLAP with those of in-memory, combining extreme performance with a metadata semantic layer and a rigorous database modeling approach, which allows easy management and control of complex BI and CPM implementations. Furthermore, the separation of the database from the applications guarantees sharing, uniqueness and consistency of information at all times, making it easy to implement cross-functional or departmental solutions. Another factor to consider is related to the ease of development: HBMP technology allows users and developers to create applications, analyses and reports without writing a single line of code (visual modeling) and without having to reload the entire database to verify the changes made, as is required by the main in-memory solutions. Price-performance The possibility to choose the most suitable in-memory configuration, amongst the three offered by HBMP technology (Full in-memory, On-demand in-memory, Hybrid in memory) and the ability to easily switch between them allow the administrator to decide whether it is more appropriate to use the RAM or disk space depending on how much or the type of data that is being used. These extensive configuration options make it easy to align infrastructural costs with business needs, achieving the best price-performance ratio for any BI and CPM solution implemented. Furthermore, the low impact of concurrent users on the amount of RAM resources required does not expose BOARD to the significant increase in costs typically associated with the growth of users in other stand alone in-memory solutions. Technological Benefits HBMP technology is not limited to the capability of storing data in-memory, reducing the need for disk I/O. It encompasses huge improvements in parallelism, data compression, and high user concurrency further enhancing BOARD s capability to handle large volumes of data and users at a high processing speed. Parallelism HBMP technology fully parallelizes BOARD processing to effectively leverage multi-core architecture. The effective spreading of the calculation load across CPU cores enables more effective workload management and a better handling of the concurrency. 8 THE NEW FRONTIER OF IN-MEMORY BUSINESS INTELLIGENCE AND CPM

9 Compression Thanks to the adoption of a new mathematical model, the HBMP technology makes it possible to map in memory the multidimensional structure of the data at bit level. This new compression scheme considerably reduces the data space required to create cubes and makes the weight of BOARD databases very similar to the most advanced associative read only products, with the remarkable difference that BOARD databases are read and write and have the capability to distribute data changes along the hierarchies. Concurrency HBMP technology drastically improves high user concurrency in BOARD. Unlike traditional in-memory techniques that struggle or require massive amounts of RAM to support large numbers of concurrent users, HBMP provides the capability to support concurrent users sharing a unique server instance, thus minimizing the impact of concurrent users on memory usage. Write-back Supported Calculated value HBMP STAND-ALONE IN-MEMORY Not supported (or supported just on a single non-calculated value) Data Spreading Supported Not supported (or poorly supported) Data model Data documentation Extensibility Scalability Flexible (Changes on the fly ) Notes File attachment Images Drill-through Cross application data model Virtually unlimited Low incidence of concurrent users on RAM resources Rigid Not supported Drill-through not supported Sealed data model Limited to existing RAM High incidence of concurrent users on RAM resources Workflow Integrated Not supported Office Integration Complete and Advanced Limited Development environment Programming-free, Visual Directly on-line Logical or physical view of data Code based Need to re-upload the data to go live Physical view of data THE NEW FRONTIER OF IN-MEMORY BUSINESS INTELLIGENCE AND CPM 9

10 HBMP: the architectural advantage Behind the important benefits offered by HBMP technology lie three fundamental architectural factors that make it superior to any other in-memory solution available today. Bi-directional algorithm The mathematical model according to which the data is indexed and mapped in-memory has been designed and built to support not only the output process (read), but also the logically inverse input processes (write). In-memory metadata HBMP technology does not just allow for in-memory data management, but also ensures the ability to run the meta-data processing in RAM. From a practical point of view this ability makes it possible to change the database structure and mapping and to see the changes reflected in applications and analyses on the fly without having to reload the physical data. Moreover, it is from the combination of the in-memory metadata processing and the bi-directional algorithm that the HBMP technology derives its Hybrid nature, i.e. the ability to administer the mapping and indexing of data in-memory, deciding whether to physically store it in RAM or on disk. Single server instance HBMP technology is characterized by the ability to support concurrent users with a single server instance. This capability makes the impact of high concurrent users on the RAM resources almost insignificant, totally differentiating HBMP from all the main in-memory technologies, which are heavily impacted. Numbers talk: HBMP benchmarks The testing activities conducted on the HBMP technology have produced outstanding results in terms of performance improvements on all main components of BOARD: Execution of the lay-out Times become two to ten times faster, with even greater improvements for complex multi-cube reports. Data loading Time is reduced from two to more than ten times. Size of database The defined data volume size of BOARD HBMP databases is from four to ten times smaller. Performing selections Execution times of selection on very large trees (millions of members) become almost instantaneous. 10 THE NEW FRONTIER OF IN-MEMORY BUSINESS INTELLIGENCE AND CPM

11 The BI and CPM market: the unique position of HBMP The table published by Gartner in the Need for Speed Powers In-Memory Business Intelligence report, gives a clear indication of the positioning of major in-memory Business Intelligence players operating within the market. In particular, it is clear that no other vendor besides BOARD can currently offer hybrid in-memory and write-back capabilities in the same product. When also extending the comparison to the Corporate Performance Management vendors, BOARD s uniqueness is even more evident: in fact, the vast majority of players in this space do not use any in-memory technology yet. Primary Use Case Hybrid In-Memory/Disk Stand-Alone In-Memory Ad hoc analysis (e.g., data exploration, pivoting) Actuate IBM (Cognos 10) JasperSoft LogiXML MicroStrategy SAP IBM (Cognos Real-time Monitoring) Microsoft QlikTech Tableau Tibco (Spotfire) What if? analysis (e.g., scenario modeling, planning) BOARD IBM (Cognos TM1) Salient Notes: (1) Stand-alone in-memory products do all processing against data held in memory; those that take a hybrid approach apply both inmemory and disk-based queries at runtime as appropriate to the workload. (2) What if? tools can also support ad hoc analysis use cases within the bounds of the predefined model. (3) This table covers only the generally available in-memory offerings of vendors covered in the 2011 Magic Quadrant for Business Intelligence Platforms. In-memory-enabled BI offerings are also available from other vendors. Source: Gartner [Need for Speed Powers In-Memory Business Intelligence] [9 June 2011] THE NEW FRONTIER OF IN-MEMORY BUSINESS INTELLIGENCE AND CPM 11

12 HBMP: The proof The Sogegross experience The company Sogegross Group is one of the top 10 Italian groups in the field of wholesale distribution. The Group currently has administrative center in Genoa and is most active in the north-central regions of Italy, with over 2,300 employees, through five distribution channels (Cash & Carry, Supermarkets and Superstores, Discount Food, Supermarkets and Superette franchises, and e-commerce), and more than 260 stores. Sogegross uses BOARD for various applications and analyses for a total BOARD database volume of around 715GB. HBMP: technology test Sogegross was one of the earliest HBMP technology beta testers worldwide, achieving extraordinary results. From 2 minutes to 10 seconds. A sales report generated using 11 different cubes required more than 2 minutes with BOARD 7.3: the same report was obtained in 10 seconds when using the HBMP technology. From 170 GB to 24 GB. The daily sales cube of the Cash & Carry channel (2-year movements in line for about 90 million records) weighed 170GB with BOARD 7, it now weighs 24GB with the HBMP technology. If these numbers describe the peak performance obtained in situations where adopting the HBMP technology will bring the most benefits, the average results obtained during the entire test are no less impressive. Loading time. there was a reduction from three to seven times. Execution time of the report. There was a general improvement, with a drastic reduction in the time required. A quantitative evaluation would not be so meaningful because the performance increase is not uniform, but it is closely related to some specific reports features (such as number of cubes, rendering incidence, use of time functions etc.). Particularly significant performance improvements have been achieved in multi-cube reports, with several cases showing execution times of around times faster. Cube Volume. Using HBMP technology cubes, volume was reduced by three to seven times. Daily cubes reported the most significant improvements. Gianni Ivaldi, CIO of Sogegross, comments: The first time we used HBMP technology the data loading was so fast compared to before that we thought there was a mistake. We then quickly found that everything worked fine, but with a different order of magnitude. This was a dramatic improvement and was achieved without modifying anything in the existing applications and analyses. Marco Staiti, Head of Infrastructure, Systems, Security and Telecommunications at Sogegross, adds: From a technical point of view we were really impressed not only by the speed of BOARD, but also by its ability to manage the workload distribution in a multicore environment very effectively. Finally, a product that is capable of exploiting all the potential of our blade server. 12 THE NEW FRONTIER OF IN-MEMORY BUSINESS INTELLIGENCE AND CPM

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